CN108859938B

CN108859938B - Method and system for automatic vehicle emergency light control for autonomous vehicles

Info

Publication number: CN108859938B
Application number: CN201810186390.4A
Authority: CN
Inventors: 朱帆; 孔旗; 罗琦; 于翔; 胡森; 杨光; 王京傲
Original assignee: Baidu USA LLC
Current assignee: Baidu USA LLC
Priority date: 2017-05-10
Filing date: 2018-03-07
Publication date: 2022-05-13
Anticipated expiration: 2038-03-07
Also published as: CN108859938A; US10507813B2; US20180326956A1

Abstract

The present application relates to a method and system for automatic vehicle emergency light control for an autonomous vehicle. In one embodiment, the ADV is determined to be decelerating based on a perception of a driving environment surrounding the ADV. Further, if there is another vehicle following the ADV, a distance between the ADV and the following vehicle and a speed of the following vehicle are determined. Based on the distance between the ADV and the following vehicle and the speed of the following vehicle, a rate of deceleration of the following vehicle required to avoid a collision with the ADV is determined. If the deceleration rate is greater than a predetermined threshold, the braking and emergency lights of the ADV are turned on to alert the following vehicle that the ADV is about to rapidly decelerate because it is treated as an emergency.

Description

Method and system for automatic vehicle emergency light control for autonomous vehicles

Technical Field

Embodiments of the present invention generally relate to operating an automotive vehicle. More particularly, embodiments of the invention relate to sending a signal to a following vehicle in response to an emergency deceleration or stop of an autonomous vehicle.

Background

Vehicles operating in an autonomous mode (e.g., unmanned) may relieve occupants, and particularly the driver, from some driving-related duties. When operating in the automatic mode, the vehicle may be navigated to various locations using onboard sensors, allowing the vehicle to drive with minimal human interaction or in some cases without any passengers.

For safety reasons it is important to maintain the distance between an automatic vehicle (ADV) and another vehicle following the ADV when driving the ADV, especially when the ADV is about to decelerate quickly, such as in the case of an emergency stop. However, sudden stops during driving are rare and are commonly used to avoid accidents, which are very dangerous and may cause other accidents, most likely from subsequent vehicles.

Disclosure of Invention

A first aspect of the present application provides a computer-implemented method for operating an autonomous vehicle, the method comprising: determining that an autonomous vehicle requires deceleration based on a perception of a driving environment surrounding the autonomous vehicle; determining a distance between the autonomous vehicle and a following vehicle that follows the autonomous vehicle and a speed of the following vehicle; estimating a first deceleration rate required by the following vehicle to avoid a possible collision with the autonomous vehicle based on the distance and the speed of the following vehicle; and in response to determining that the first deceleration rate is greater than a first predetermined threshold, turn on brake and emergency lights of the autonomous vehicle to alert the following vehicle that the autonomous vehicle is about to rapidly decelerate.

In certain embodiments, the emergency lights include a left turn signal light and a right turn signal light.

In certain embodiments, the method further comprises: generating a braking command and issuing the braking command to the autonomous vehicle for deceleration.

In certain embodiments, the brake lights and emergency lights of the autonomous vehicle are turned on before issuing the braking command to the autonomous vehicle to decelerate.

In certain embodiments, the brake light is only turned on if the first deceleration rate is less than or equal to the first predetermined threshold.

A second aspect of the present application provides a non-transitory machine-readable medium having instructions stored therein, which when executed by a processor, cause the processor to perform operations for maneuvering an autonomous vehicle, the operations comprising: determining that an autonomous vehicle requires deceleration based on a perception of a driving environment surrounding the autonomous vehicle; determining a distance between the autonomous vehicle and a following vehicle that follows the autonomous vehicle and a speed of the following vehicle; estimating a first deceleration rate required by the following vehicle to avoid a possible collision with the autonomous vehicle based on the distance and the speed of the following vehicle; and in response to determining that the first deceleration rate is greater than a first predetermined threshold, turn on brake and emergency lights of the autonomous vehicle to alert the following vehicle that the autonomous vehicle is about to rapidly decelerate.

In certain embodiments, the operations further comprise: generating a braking command and issuing the braking command to the autonomous vehicle for deceleration.

In certain embodiments, the brake light and the emergency light of the autonomous vehicle are turned on before the brake command is issued to the autonomous vehicle for deceleration.

A third aspect of the present application provides a data processing system comprising: a processor; and a memory coupled to the processor and storing instructions that, when executed by the processor, cause the processor to perform operations for maneuvering an autonomous vehicle, the operations comprising: determining that an autonomous vehicle requires deceleration based on a perception of a driving environment surrounding the autonomous vehicle; determining a distance between the autonomous vehicle and a following vehicle that follows the autonomous vehicle and a speed of the following vehicle; estimating a first deceleration rate required by the following vehicle to avoid a possible collision with the autonomous vehicle based on the distance and the speed of the following vehicle; and in response to determining that the first deceleration rate is greater than a first predetermined threshold, turn on brake and emergency lights of the autonomous vehicle to alert the following vehicle that the autonomous vehicle is about to rapidly decelerate.

Drawings

Embodiments of the invention are illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings in which like reference numerals refer to similar elements.

Fig. 1 is a block diagram illustrating a network system according to an embodiment of the present invention.

Fig. 2 is a block diagram showing an example of an automated vehicle according to an embodiment of the present invention.

FIG. 3 is a block diagram illustrating an example of a perception and planning system for use with an automated vehicle according to one embodiment of the present invention.

FIG. 4 is a process flow diagram illustrating an example of a process of operating an autonomous vehicle in accordance with one embodiment of the invention.

Fig. 5 illustrates a data structure representing a lamp mode mapping table according to an embodiment of the present invention.

FIG. 6 is a flow chart illustrating a process of operating an autonomous vehicle in accordance with one embodiment of the invention.

FIG. 7 is a flow chart illustrating a process of operating an autonomous vehicle in accordance with one embodiment of the invention.

FIG. 8 is a block diagram illustrating a data processing system in accordance with one embodiment.

Detailed Description

Various embodiments and aspects of the invention will be described with reference to details discussed below, and the accompanying drawings will illustrate the various embodiments. The following description and drawings are illustrative of the invention and are not to be construed as limiting the invention. Numerous specific details are described to provide a thorough understanding of various embodiments of the invention. However, in certain instances, well-known or conventional details are not described in order to provide a concise discussion of embodiments of the present inventions.

Reference in the specification to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment.

According to some embodiments, the brake and emergency lights of an automatic vehicle (ADV) are used to automatically signal to a following vehicle that the ADV is about to rapidly decelerate, for example by turning on both the brake and emergency lights of the ADV. Typically, when the ADV requires deceleration, a braking command will be generated and issued to the ADV. As a result, the brake light will turn on as pressure is applied to the brake pedal. From the following vehicle's perspective, the driver of the following vehicle cannot determine whether the preceding vehicle is decelerating slowly or rapidly based on the brake lights. However, if both the brake lamp and the hazard lamp are turned on, the driver of the following vehicle is more likely to recognize it as an emergency and the driver of the following vehicle may press the brake pedal very hard, which may avoid a collision with the preceding vehicle. In an embodiment of the present invention, the emergency light may include a left turn signal light and a right turn signal light.

In one embodiment, the determination that the ADV is about to decelerate is based on a perception of the driving environment surrounding the ADV. Further, if there is another vehicle following the ADV, a distance between the ADV and the following vehicle and a speed of the following vehicle are determined. Based on the distance between the ADV and the following vehicle and the speed of the following vehicle, a rate of deceleration of the following vehicle required to avoid a collision with the ADV is determined. If the deceleration rate is greater than a predetermined threshold, the braking and emergency lights of the ADV are turned on to alert the following vehicle that the ADV is about to rapidly decelerate because it is treated as an emergency. If the deceleration rate of the following vehicle is less than or equal to the predetermined threshold, only the brake lamp is turned on since it is treated as a conventional or ordinary deceleration process only. As a result, the following vehicle can carry out any required measures to avoid any possible collision, depending on the specific situation.

According to another embodiment, a rate of deceleration of the ADV is determined. If the rate of deceleration of the ADV is greater than a predetermined threshold, the ADV and the driving conditions of the following vehicle are analyzed to determine if the emergency lights should be turned on. That is, the emergency light will only turn on when the ADV is rapidly decelerating in an emergency. Otherwise, if the rate of deceleration of the ADV is less than or equal to the predetermined threshold, then the condition will be treated as a normal braking condition and only the brake lights will be turned on in response to the braking command.

Fig. 1 is a block diagram showing a network configuration of an automatic vehicle according to an embodiment of the present invention. Referring to fig. 1, a network configuration 100 includes an autonomous vehicle 101 that may be communicatively coupled to one or more servers 103-104 through a network 102. Although one autonomous vehicle is shown, multiple autonomous vehicles may be coupled to each other and/or to servers 103-104 through network 102. The network 102 may be any type of network, such as a wired or wireless Local Area Network (LAN), a Wide Area Network (WAN) such as the Internet, a cellular network, a satellite network, or a combination thereof. The servers 103-104 may be any type of server or cluster of servers, such as a network or cloud server, an application server, a backend server, or a combination thereof. The servers 103 to 104 may be data analysis servers, content servers, traffic information servers, map and point of interest (MPOI) servers, or location servers, etc.

An autonomous vehicle refers to a vehicle that may be configured to be in an autonomous mode in which the vehicle navigates through the environment with little or no input from the driver. Such an autonomous vehicle may include a sensor system having one or more sensors configured to detect information related to the vehicle operating environment. The vehicle and its associated controller use the detected information to navigate through the environment. The autonomous vehicle 101 may operate in a manual mode, in a fully automatic mode, or in a partially automatic mode.

In one embodiment, autonomous vehicle 101 includes, but is not limited to: a perception and planning system 110, a vehicle control system 111, a wireless communication system 112, a user interface system 113, an information server (not shown), and a sensor system 115. The autonomous vehicle 101 may also include certain common components included in conventional vehicles, such as: engines, wheels, steering wheels, transmissions, etc., which may be controlled by the vehicle control system 111 and/or the sensing and planning system 110 using various communication signals and/or commands, such as: an acceleration signal or command, a deceleration signal or command, a steering signal or command, a braking signal or command, etc.

The components 110-115 may be communicatively coupled to each other via an interconnect, bus, network, or combination thereof. For example, the components 110-115 may be communicatively coupled to one another via a Controller Area Network (CAN) bus. The CAN bus is a vehicle bus standard designed to allow microcontrollers and devices to communicate with each other in applications without a host. It is a message-based protocol originally designed for multiplexed electrical wiring within automobiles, but is also used in many other environments.

Referring now to fig. 2, in one embodiment, the sensor system 115 includes, but is not limited to, one or more cameras 211, a Global Positioning System (GPS) unit 212, an Inertial Measurement Unit (IMU)213, a radar unit 214, and a light detection and ranging (LIDAR) unit 215. The GPS system 212 may include a transceiver operable to provide information regarding the location of the autonomous vehicle. The IMU unit 213 may sense position and orientation changes of the autonomous vehicle based on inertial acceleration. Radar unit 214 may represent a system that utilizes radio signals to sense objects within the local environment of an autonomous vehicle. In some embodiments, in addition to sensing an object, radar unit 214 may additionally sense a speed and/or heading of the object. The LIDAR unit 215 may use a laser to sense objects in the environment in which the autonomous vehicle is located. The LIDAR unit 215 may include one or more laser sources, laser scanners, and one or more detectors, among other system components. The camera 211 may include one or more devices used to capture images of the environment surrounding the autonomous vehicle. The camera 211 may be a still camera and/or a video camera. The camera may be mechanically movable, for example, by mounting the camera on a rotating and/or tilting platform.

The sensor system 115 may also include other sensors, such as: sonar sensors, infrared sensors, steering sensors, throttle sensors, brake sensors, and audio sensors (e.g., microphones). The audio sensor may be configured to collect sound from an environment surrounding the autonomous vehicle. The steering sensor may be configured to sense a steering angle of a steering wheel, wheels of a vehicle, or a combination thereof. The throttle sensor and the brake sensor sense a throttle position and a brake position of the vehicle, respectively. In some cases, the throttle sensor and the brake sensor may be integrated into an integrated throttle/brake sensor.

In one embodiment, the vehicle control system 111 includes, but is not limited to: a steering unit 201, a throttle unit 202 (also referred to as an acceleration unit), and a brake unit 203. The steering unit 201 is used to adjust the direction or forward direction of the vehicle. The throttle unit 202 is used to control the speed of the motor or engine, which in turn controls the speed and acceleration of the vehicle. The brake unit 203 decelerates the vehicle by providing friction to decelerate the wheels or tires of the vehicle. It should be noted that the components shown in fig. 2 may be implemented in hardware, software, or a combination thereof.

Returning to fig. 1, the wireless communication system 112 allows communication between the autonomous vehicle 101 and external systems such as devices, sensors, other vehicles, and the like. For example, the wireless communication system 112 may be in direct wireless communication with one or more devices, or in wireless communication via a communication network, such as with the servers 103-104 through the network 102. The wireless communication system 112 may use any cellular communication network or Wireless Local Area Network (WLAN), for example, using WiFi, to communicate with another component or system. The wireless communication system 112 may communicate directly with devices (e.g., passenger's mobile device, display device, speaker within the vehicle 101), for example, using infrared links, bluetooth, etc. The user interface system 113 may be part of a peripheral device implemented within the vehicle 101, including, for example, a keypad, a touch screen display device, a microphone, and speakers, among others.

Some or all of the functions of the autonomous vehicle 101 may be controlled or managed by the perception and planning system 110, particularly when operating in an autonomous driving mode. The awareness and planning system 110 includes the necessary hardware (e.g., processors, memory, storage devices) and software (e.g., operating systems, planning and route planning programs) to receive information from the sensor system 115, the control system 111, the wireless communication system 112, and/or the user interface system 113, process the received information, plan a route or path from the origin to the destination, and then drive the vehicle 101 based on the planning and control information. Alternatively, the sensing and planning system 110 may be integrated with the vehicle control system 111.

For example, a user who is a passenger may specify a start location and a destination of a trip, e.g., via a user interface. The perception and planning system 110 obtains trip related data. For example, the sensing and planning system 110 may obtain location and route information from an MPOI server, which may be part of the servers 103-104. The location server provides location services and the MPOI server provides map services and POIs for certain locations. Alternatively, such location and MPOI information may be cached locally in persistent storage of the sensing and planning system 110.

The perception and planning system 110 may also obtain real-time traffic information from a traffic information system or server (TIS) as the autonomous vehicle 101 moves along the route. It should be noted that the servers 103 to 104 may be operated by third party entities. Alternatively, the functionality of the servers 103-104 may be integrated with the perception and planning system 110. Based on the real-time traffic information, MPOI information, and location information, as well as real-time local environmental data (e.g., obstacles, objects, nearby vehicles) detected or sensed by sensor system 115, perception and planning system 110 may plan an optimal route and drive vehicle 101, e.g., via control system 111, according to the planned route to safely and efficiently reach a designated destination.

The server 103 may be a data analysis system that performs data analysis services for various clients. In one embodiment, data analysis system 103 includes a data collector 121 and a machine learning engine 122. The data collector 121 collects driving statistics 123 from various vehicles (either autonomous vehicles or ordinary vehicles driven by human drivers). The driving statistics 123 include information representing driving commands issued at different points in time (e.g., throttle, brake, steering commands) and vehicle responses (e.g., speed, acceleration, deceleration, driving direction) captured and measured by sensors of the vehicle. The driving statistics 123 may also include information describing the driving environment at different points in time, such as a route (including a start location and a destination location), MPOI, road conditions, weather conditions, and so forth.

Based on the driving statistics 123, the machine learning engine 122 generates or trains a rule set, algorithm, and/or predictive model 124 for various purposes. In one embodiment, rules 124 include a set of rules for determining whether an ADV is about to slow down and how aggressive the slowing down will be. Rules 124 include thresholds for determining whether the ADV is to be decelerated in a normal or regular manner or whether the ADV is to be rapidly decelerated in an emergency manner. The threshold may be derived from the driving behavior of various drivers based on the driving statistics 123. For example, if the rate of deceleration of the ADV exceeds a threshold, the deceleration may be considered a rapid deceleration or an emergency stop. If the rate of deceleration is less than or equal to the threshold, the deceleration will be considered a normal deceleration or a conventional deceleration or stop.

Further, as part of the rules 124, the machine learning engine 122 may determine another deceleration threshold for the vehicle following the ADV, which may be used to determine whether the hazard lights should be turned on when the ADV is about to decelerate quickly. Such deceleration thresholds are also derived based on the driving behavior of various drivers driving various vehicles. For example, if the deceleration of the following vehicle required to avoid a collision with the preceding vehicle exceeds a threshold, the following vehicle needs to perform an emergency deceleration or stop. In this case, the ADV may turn on both the brake and emergency lights to signal to the following vehicle that the ADV is about to perform an emergency deceleration or emergency stop.

Alternatively, the machine learning engine 122 may derive the distance threshold based on the driving statistics 123, where the driving statistics 123 are used to determine whether deceleration of the ADV is deemed urgent deceleration from the perspective of the following vehicle. For example, when the ADV is about to decelerate, both the brake and emergency lights may be turned on if the distance between the ADV and the following vehicle is less than a distance threshold. In one embodiment, the brake and emergency lights may be turned on if the distance decreases below the distance threshold, regardless of the rate of deceleration or speed of the ADV and/or the following vehicle. The basic principle of the method is that if the following vehicle is too close to the ADV, the likelihood of a collision between the ADV and the following vehicle will increase significantly when the ADV decelerates, regardless of the rate of deceleration of the ADV and the following vehicle. These thresholds may be determined based on past event statistics.

Further, machine learning engine 122 can determine an average distance between two vehicles under a particular driving environment from driving statistics 123. The machine learning engine 122 determines and generates a set of distance thresholds for different driving scenarios or environments as part of the rules 124. In addition, certain brake light and/or emergency light flashing patterns may be configured for different driving scenarios. The rules 124 may then be uploaded into the autonomous vehicle in the form of a data structure (e.g., table, database) to be used to signal to the following vehicle in real time that the ADV is about to rapidly decelerate in an emergency. The distance threshold may be determined based on safe traffic regulations or safe distances required for the vehicle to come to a complete stop in an emergency.

FIG. 3 is a block diagram illustrating an example of a perception and planning system for use with an automated vehicle according to one embodiment of the present invention. The system 300 may be implemented as part of the autonomous vehicle 101 of fig. 1, including but not limited to the sensing and planning system 110, the control system 111, and the sensor system 115. Referring to fig. 3, the perception and planning system 110 includes, but is not limited to: a location module 301, a perception module 302, a decision module 303, a planning module 304, a control module 305, a brake light controller 306, an emergency light controller 307, and a vehicle warning module 308.

Some or all of the modules 301 to 308 may be implemented in software, hardware, or a combination thereof. For example, the modules may be installed in persistent storage 352, loaded into memory 351, and executed by one or more processors (not shown). It should be noted that some or all of these modules may be communicatively coupled to or integrated with some or all of the modules of the vehicle control system 111 of fig. 2. Some of the modules 301 to 308 may be integrated together into an integrated module.

The positioning module 301 (also referred to as a map and route module) manages any data related to the user's journey or route. The positioning module 301 determines the current location of the autonomous vehicle 101 (e.g., using the GPS unit 201) and manages any data related to the user's trip or route. The user may, for example, log in via a user interface and specify a starting location and a destination for the trip. The positioning module 301 communicates with other components of the autonomous vehicle 101 to obtain trip related data, such as map and route information 311. For example, the location module 301 may obtain location and route information from a location server and a map and poi (mpoi) server. The location server provides location services and the MPOI server provides map services and POIs for certain locations and may thus be cached as part of the map and route information 311. The location module 301 may also obtain real-time traffic information from a traffic information system or server as the autonomous vehicle 101 moves along the route.

Based on the sensor data provided by sensor system 115 and the positioning information obtained by positioning module 301, perception module 302 determines a perception of the surrounding environment. The perception information may represent what an average driver would perceive around the vehicle the driver is driving. Perception may include, for example, lane configuration in the form of an object (e.g., a straight lane or a curved lane), a traffic light signal, a relative position of another vehicle, a pedestrian, a building, a crosswalk, or other traffic-related indicia (e.g., a stop sign, a yield sign), and so forth.

The perception module 302 may include a computer vision system or functionality of a computer vision system to process and analyze images captured by one or more cameras to identify objects and/or features in an automated vehicle environment. The objects may include traffic signals, road boundaries, other vehicles, pedestrians, and/or obstacles, etc. Computer vision systems may use object recognition algorithms, video tracking, and other computer vision techniques. In some embodiments, the computer vision system may map the environment, track objects, and estimate the speed of objects, among other things. The perception module 302 may also detect objects based on other sensor data provided by other sensors, such as radar and/or LIDAR.

For each subject, the decision module 303 makes a decision on how to treat the subject. For example, for a particular object (e.g., another vehicle in a crossing route) and metadata describing the object (e.g., speed, direction, turn angle), the decision module 303 decides how to encounter the object (e.g., cut, yield, stop, exceed). The decision module 303 may make such a decision according to a rule set, such as traffic rules or driving rules 312, which may be stored in persistent storage 352.

Based on the decisions for each of the perceived objects, the planning module 304 plans a path or route and driving parameters (e.g., distance, speed, and/or turn angle) for the automated vehicle. In other words, for a given object, the decision module 303 decides what to do with the object, and the planning module 304 determines how to do. For example, for a given subject, decision module 303 may decide to exceed the subject, and planning module 304 may determine whether to exceed to the left or right of the subject. The planning and control data is generated by the planning module 304, including information describing how the vehicle 101 will move in the next movement cycle (e.g., the next route/path segment). For example, the planning and control data may instruct vehicle 101 to move 10 meters at a speed of 30 miles per hour (mph), and then change to the right lane at a speed of 25 mph.

Based on the planning and control data, the control module 305 controls and drives the autonomous vehicle by sending appropriate commands or signals to the vehicle control system 111 according to the route or path defined by the planning and control data. The planning and control data includes sufficient information to drive the vehicle from a first point to a second point of the route or path at different points in time along the route or route using appropriate vehicle settings or driving parameters (e.g., throttle, brake, and turn commands).

It should be noted that the decision module 303 and the planning module 304 may be integrated as an integrated module. The decision module 303/planning module 304 may include a navigation system or functionality of a navigation system to determine a driving path of an autonomous vehicle. For example, the navigation system may determine a series of speeds and heading directions for enabling the automated vehicle to move along the following paths: the path substantially avoids perceived obstacles while advancing the autonomous vehicle along a roadway-based path to a final destination. The destination may be set based on user input via the user interface system 113. The navigation system may dynamically update the driving path while the autonomous vehicle is operating. The navigation system may combine data from the GPS system and one or more maps to determine a driving path for the autonomous vehicle.

The decision module 303/planning module 304 may also include a collision avoidance system or the functionality of a collision avoidance system to identify, assess, and avoid or otherwise negotiate potential obstacles in the environment of the autonomous vehicle. For example, a collision avoidance system may implement a change in the navigation of an automated vehicle by: one or more subsystems in the control system 111 are operated to take a turning maneuver, a braking maneuver, etc. The collision avoidance system may automatically determine a feasible obstacle avoidance maneuver based on surrounding traffic patterns, road conditions, and the like. The collision avoidance system may be configured such that no turn-changing maneuvers are taken when other sensor systems detect vehicles, building obstacles, etc. located in adjacent areas into which the autonomous vehicle will change direction. The collision avoidance system may automatically select maneuvers that are both available and that maximize the safety of the occupants of the autonomous vehicle. The collision avoidance system may select an avoidance maneuver that is predicted to cause a minimum amount of acceleration to occur in the passenger compartment of the autonomous vehicle.

In one embodiment, the vehicle alert module or logic 308 is configured to: the driving behavior of the following vehicle is monitored based on the perception information about the vehicle following the automatic vehicle, which can be perceived through the perception module 302. The vehicle alert module 308 may be integrated with other components, such as the decision module 303. When the perception module of ADV101 perceives the following vehicle based on the sensed data provided by sensor system 115, decision module 303 is invoked to decide how to treat the following vehicle. In one embodiment, vehicle perception module 302 determines a distance between ADV101 and a following vehicle.

In one embodiment, when ADV101 is about to decelerate based on a perception of the driving environment surrounding ADV101, planning module 304 and/or control module 305 determines a rate of deceleration required for ADV101 to slow down or achieve a complete stop. For example, based on a distance between the ADV and an object (e.g., another vehicle, a pedestrian, a static obstacle) located in front of the ADV, the planning module 304 and/or the control module 305 determines a rate of deceleration required to avoid a possible collision with the object based on the distance and a current speed of the ADV (or a relative speed of the ADV with respect to the object).

In one embodiment, the desired deceleration rate of the ADV is communicated to the vehicle alert module 308. In response, the vehicle alert module 308 determines whether the deceleration is a conventional deceleration (also referred to as a soft deceleration) or a rapid deceleration (also referred to as a jerk deceleration, an emergency deceleration, or a stop). For example, the vehicle alert module 308 compares the deceleration rate to a predetermined deceleration threshold. The predetermined deceleration threshold may be obtained from a rule 124, wherein the rule 124 may be determined based on data analysis of a large amount of driving statistics, as described above. In one embodiment, the predetermined deceleration threshold of the ADV is approximately 2 meters per square second (m/s)²)。

In one embodiment, if it is a conventional deceleration, the vehicle alert module 308 may do nothing and cause the planning module 304 and the control module 305 to perform the normal deceleration process, in which case the brake lights will be turned on in response to a brake command issued to the vehicle platform 310. Alternatively, according to another embodiment, the vehicle alert module 308 may instruct the brake light controller 306 to turn on one or more brake lights 325 of the ADV prior to issuing a braking command to the vehicle platform 310. Turning on the brake lights before issuing a braking command can provide sufficient warning and additional lead time to any vehicle that may follow or be behind the ADV on the same lane as the ADV or on a lane adjacent to the ADV's lane to react.

According to one embodiment, in response to the rate of deceleration of the ADV received from the planning module 304 and/or the control module 305, the vehicle alert module 308 determines whether there are any vehicles following the ADV, e.g., based on the perception information received from the perception module 302 and/or the prediction information received from the decision module 303. If there is a vehicle following the ADV, the vehicle alert module 308 invokes the perception module 302 to determine the distance between the ADV and the following vehicle and the speed of the following vehicle.

According to one embodiment, if the distance between the ADV and the following vehicle is less than a predetermined distance threshold, the vehicle alert module 308 may instruct the brake light controller 306 and the emergency light controller 307 to turn on both the brake light 325 and the emergency light 326 of the ADV at or before the issuance of the braking command. Otherwise, if the distance is greater than or equal to the predetermined distance threshold, the vehicle alert module 308 may turn on only the brake lamp 325 via the brake lamp controller 306. Alternatively, the vehicle alert module 308 may not perform any operations and cause the planning module 304 and/or the control module 305 to perform a conventional braking process, in which case the brake lights 325 will be turned on in response to a braking command. The predetermined distance threshold may be defined as part of the rules 124.

According to another embodiment, based on the distance and speed of the following vehicle, the vehicle alert module 308 may estimate the deceleration rate required by the following vehicle to avoid a collision with the ADV as the ADV decelerates. The deceleration rate of the following vehicle may be estimated based on a rule set or a predictive model that may be part of the rules 124. The deceleration rate of the following vehicle may be estimated based on the distance between the ADV and the following vehicle, the speed of the ADV, the speed of the following vehicle, and/or the deceleration rate of the ADV.

If the deceleration rate of the following vehicle is greater than the predetermined deceleration threshold, the vehicle alert module 308 may pass through the brake light controller 306 and the brake light controller before issuing the brake command or at the time of issuing the brake commandThe emergency light controller 307 turns on both the ADV's stop light 325 and emergency light 326, respectively. Otherwise, if the deceleration rate of the following vehicle is less than or equal to the predetermined deceleration threshold, the vehicle alert module 308 may turn on only the brake lamp 325 via the brake lamp controller 306. Alternatively, the vehicle alert module 308 may not perform any operations and cause the planning module 304 and/or the control module 305 to perform a conventional braking process, in which case the brake lights 325 will be turned on in response to a braking command. The predetermined deceleration threshold may be defined as part of the rules 124. In one embodiment, the predetermined deceleration threshold is approximately 1m/s²。

According to yet another embodiment, based on the distance and the speed of the following vehicle (e.g., taking into account the speed and deceleration rate of the ADV), the vehicle alert module 308 may estimate the time required for the following vehicle to come to a complete stop to avoid a collision with the ADV as the ADV decelerates. If the time required is less than the predetermined time threshold, the vehicle alert module 308 may turn on both the ADV's brake light 325 and emergency light 326 via the brake light controller 306 and emergency light controller 307, respectively, at or before the issuance of the braking command. Otherwise, if the time required is greater than or equal to the predetermined time threshold, the vehicle alert module 308 may turn on only the brake lamp 325 via the brake lamp controller 306. Alternatively, the vehicle alert module 308 may not perform any operations and cause the planning module 304 and/or the control module 305 to perform a conventional braking process, in which case the brake lights 325 will be turned on in response to a braking command. The predetermined time threshold may be defined as part of the rules 124.

In one embodiment, the stop light 325 and/or the emergency light 326 may be turned on according to a predefined blinking pattern. The vehicle alert module 308 looks up the lamp pattern map or data structure to determine whether the distance between the ADV and the following vehicle is less than a predetermined threshold, whether the desired deceleration of the following vehicle is greater than a predetermined threshold, or whether the time required for the following vehicle to achieve a complete stop is less than a predetermined threshold. The lamp pattern map may be configured offline by an analysis system (e.g., the machine learning engine 122 of the data analysis system 103) based on a large amount of driving statistics. In one embodiment, the light mode mapping table maps a threshold (e.g., distance, deceleration, or time threshold) to a particular light mode to control the brake lights and/or emergency lights such that the brake/emergency lights illuminate or flash according to the corresponding light or flash mode. The lighting or blinking pattern may include: how long the brake/emergency lights will be on, at what frequency the brake/emergency lights will flash, or a combination thereof (e.g., on and off pattern), etc. Fig. 5 illustrates one example of a brake lamp/emergency lamp mode map.

Referring now to fig. 5, a lamp pattern mapping table 500 may be configured offline by the data analysis system 103 as part of the rules 124 based on previous driving statistics. In one embodiment, the lamp mode mapping table 500 includes a plurality of mapping entries. Each mapping entry maps the driving scene 501 to a threshold 502 (e.g., a distance threshold, a deceleration threshold, or a time threshold) and a light pattern 503. The driving scenario 501 may be one or more of a distance between the ADV and the following vehicle, a speed of the ADV, a speed of the following vehicle, a rate of deceleration of the ADV, and a rate of deceleration of the following vehicle. The rationale for this is that the distance, rate of deceleration, or time required to achieve a rapid deceleration or complete stop from different speeds may be different. For safety reasons, it is desirable to leave a sufficient distance between the leading vehicle and the following vehicle so that the following vehicle can come to a complete stop in response to an emergency without colliding with the leading vehicle.

Alternatively, the driving scenario 501 may be a combination of one or more of road conditions (e.g., rough roads, smooth roads), weather conditions (e.g., wet conditions, snowy conditions), weight of the vehicle (e.g., number of occupants), speed of the vehicle, width of the road, curvature of the road, traffic conditions, and the like. The weight and speed of the following vehicle may be estimated based on the perception data of the following vehicle. Different driving scenarios may have different thresholds 502. For example, a wet or rough road may require a longer distance to achieve a complete stop, and thus the threshold 502 may be greater. When the distance between an ADV (e.g., leading vehicle) and a following vehicle driven under the same or similar driving scenario 501 is detected to be less than a corresponding threshold 502, the ADV's brake/emergency lights may be turned on to signal the following vehicle to slow it down.

For the lamp mode mapping table 500, different thresholds may be maintained for different driving scenarios. In one embodiment, a light pattern 503 is specified for each of the driving scenario 501 and the threshold 502. That is, under a particular driving scenario (e.g., road conditions, speed, weather), if the distance between two vehicles decreases below the corresponding threshold 502, the brake/emergency lights of the ADV may be turned on according to the light pattern 503 specified in the map entry. For example, if the distance between two vehicles decreases to within a first threshold, the brake/hazard lights of the ADV may turn on for a first period of time. However, if the following vehicle does not slow down or the distance between two vehicles continues to decrease, the brake/emergency lights of the ADV may turn on for a longer period of time or blink more frequently. This mode typically signals an emergency situation to the driver of the following vehicle.

FIG. 6 is a flow chart illustrating a process of operating an autonomous vehicle according to one embodiment of the invention. Process 600 may be performed by processing logic that may comprise software, hardware, or a combination thereof. For example, the process 600 may be performed by the vehicle alert module 308. Referring to fig. 6, in operation 601, processing logic determines that an ADV is about to decelerate based on a perception of the driving environment surrounding the ADV. In operation 602, the processing logic determines a distance between the ADV and a vehicle following the ADV and a speed of the following vehicle. In operation 603, the processing logic calculates a deceleration rate required by the following vehicle to avoid a possible collision with the ADV. The deceleration rate of the following vehicle may be estimated based on the distance between the ADV and the following vehicle, the speed of the ADV, the speed of the following vehicle, and/or the deceleration rate of the ADV. In operation 604, if the desired deceleration rate is greater than the predetermined threshold, processing logic turns on the brake and emergency lights of the ADV to alert the following vehicle that the ADV is about to rapidly decelerate.

Fig. 7 is a flowchart illustrating a process of operating an automatic vehicle according to another embodiment of the present invention. Process 700 may be performed by processing logic that may comprise software, hardware, or a combination thereof. For example, the process 700 may be performed by the vehicle alert module 308. Referring to fig. 7, in operation 701, processing logic determines a first rate of deceleration of the ADV based on a perception of a driving environment surrounding the ADV. In operation 702, processing logic determines whether the first deceleration rate is greater than a first predetermined threshold. If not, in operation 707, a braking command is generated and issued based on the first deceleration rate, wherein the brake lights will be turned on in response to the braking command.

If the first deceleration rate is greater than the first predetermined threshold, then in operation 703, the processing logic determines a second deceleration rate required for the vehicle following the ADV to decelerate to avoid a potential collision with the ADV. The second rate of deceleration of the following vehicle may be estimated based on a distance between the ADV and the following vehicle, a speed of the ADV, a speed of the following vehicle, and/or the first rate of deceleration of the ADV. In operation 704, processing logic determines whether the second deceleration rate is greater than a second predetermined threshold. If so, in operation 705, processing logic turns on the ADV's emergency light. In operation 706, processing logic turns on the brake lights of the ADV. If the second deceleration rate is less than or equal to the second predetermined threshold, then the brake light is turned on only in operation 706. In operation 707, a braking command is generated and issued based on the first deceleration rate to slow the ADV. The brake lights and/or emergency lights may be turned on in operations 705-706 before issuing a brake command in operation 707.

It should be noted that some or all of the components as shown and described above may be implemented in software, hardware, or a combination thereof. For example, such components may be implemented as software installed and stored in a persistent storage device, which may be loaded into and executed by a processor (not shown) to perform the processes or operations described throughout this application. Alternatively, such components may be implemented as executable code programmed or embedded into dedicated hardware, such as an integrated circuit (e.g., an application specific integrated circuit or ASIC), a Digital Signal Processor (DSP) or Field Programmable Gate Array (FPGA), which is accessible via a respective driver and/or operating system from an application. Further, such components may be implemented as specific hardware logic within a processor or processor core as part of an instruction set accessible by software components through one or more specific instructions.

FIG. 8 is a block diagram illustrating an example of a data processing system that may be used with one embodiment of the invention. For example, system 1500 may represent any of the data processing systems described above that perform any of the processes or methods described above, such as, for example, perception and planning system 110 or any of servers 103-104 of fig. 1. System 1500 may include many different components. These components may be implemented as Integrated Circuits (ICs), portions of integrated circuits, discrete electronic devices or other modules adapted for a circuit board, such as a motherboard or add-in card of a computer system, or as components otherwise incorporated within a chassis of a computer system.

It should also be noted that system 1500 is intended to illustrate a high-level view of many components of a computer system. However, it is to be understood that some embodiments may have additional components and, further, other embodiments may have different arrangements of the components shown. System 1500 may represent a desktop computer, a laptop computer, a tablet computer, a server, a mobile phone, a media player, a Personal Digital Assistant (PDA), a smart watch, a personal communicator, a gaming device, a network router or hub, a wireless Access Point (AP) or repeater, a set-top box, or a combination thereof. Further, while only a single machine or system is illustrated, the term "machine" or "system" shall also be taken to include any collection of machines or systems that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein.

In one embodiment, the system 1500 includes a processor 1501, memory 1503, and devices 1505-1508 connected by a bus or interconnect 1510. Processor 1501 may represent a single processor or multiple processors including a single processor core or multiple processor cores. Processor 1501 may represent one or more general-purpose processors, such as a microprocessor, Central Processing Unit (CPU), or the like. More specifically, processor 1501 may be a Complex Instruction Set Computing (CISC) microprocessor, Reduced Instruction Set Computing (RISC) microprocessor, Very Long Instruction Word (VLIW) microprocessor, or a processor implementing other instruction sets, or processors implementing a combination of instruction sets. Processor 1501 may also be one or more special-purpose processors, such as an Application Specific Integrated Circuit (ASIC), a cellular or baseband processor, a Field Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), a graphics processor, a network processor, a communications processor, a cryptographic processor, a coprocessor, an embedded processor, or any other type of logic capable of processing instructions.

Processor 1501 (which may be a low-power multi-core processor socket such as an ultra-low voltage processor) may serve as a main processing unit and central hub for communicating with the various components of the system. Such a processor may be implemented as a system on a chip (SoC). The processor 1501 is configured to execute instructions for performing the operations and steps discussed herein. The system 1500 may also include a graphics interface in communication with an optional graphics subsystem 1504, which graphics subsystem 1504 may include a display controller, a graphics processor, and/or a display device.

Processor 1501 may be in communication with memory 1503, which in one embodiment may be implemented via multiple memory devices to provide a given amount of system storage. The memory 1503 may include one or more volatile storage (or memory) devices such as Random Access Memory (RAM), dynamic RAM (dram), synchronous dram (sdram), static RAM (sram), or other types of storage devices. Memory 1503 may store information including sequences of instructions that are executed by processor 1501, or any other device. For example, executable code and/or data for various operating systems, device drivers, firmware (e.g., an input output basic system or BIOS), and/or applications may be loaded into memory 1503 and executed by processor 1501. The operating system may be any type of operating system, for example, a Robotic Operating System (ROS), from

Of a company

Operating System, Mac from apple Inc

From

Of a company

LINUX, UNIX, or other real-time or embedded operating systems.

System 1500 may also include I/O devices such as devices 1505 through 1508, including a network interface device 1505, an optional input device 1506, and other optional I/O devices 1507. Network interface device 1505 may include a wireless transceiver and/or a Network Interface Card (NIC). The wireless transceiver may be a WiFi transceiver, an infrared transceiver, a bluetooth transceiver, a WiMax transceiver, a wireless cellular telephone transceiver, a satellite transceiver (e.g., a Global Positioning System (GPS) transceiver), or other Radio Frequency (RF) transceiver, or a combination thereof. The NIC may be an ethernet card.

The input device 1506 may include a mouse, a touch pad, a touch-sensitive screen (which may be integrated with the display device 1504), a pointing device (such as a stylus) and/or a keyboard (e.g., a physical keyboard or a virtual keyboard displayed as part of the touch-sensitive screen). For example, the input device 1506 may include a touch screen controller coupled to a touch screen. Touch screens and touch screen controllers, for example, may detect contact and movement or discontinuities thereof using any of a variety of touch sensitive technologies, including but not limited to capacitive, resistive, infrared, and surface acoustic wave technologies, as well as other proximity sensor arrays or other elements for determining one or more points of contact with the touch screen.

The I/O devices 1507 may include audio devices. The audio device may include a speaker and/or microphone to facilitate voice-enabled functions, such as voice recognition, voice replication, digital recording, and/or telephony functions. Other I/O devices 1507 may also include Universal Serial Bus (USB) ports, parallel ports, serial ports, printers, network interfaces, bus bridges (e.g., PCI-PCI bridges), sensors (e.g., such as accelerometer motion sensors, gyroscopes, magnetometers, light sensors, compasses, proximity sensors, etc.), or combinations thereof. The device 1507 may also include an imaging processing subsystem (e.g., a camera) that may include an optical sensor, such as a Charge Coupled Device (CCD) or Complementary Metal Oxide Semiconductor (CMOS) optical sensor, for facilitating camera functions, such as recording photographs and video clips. Certain sensors can be coupled to interconnect 1510 via a sensor hub (not shown), while other devices, such as a keyboard or thermal sensors, can be controlled by an embedded controller (not shown) depending on the particular configuration or design of system 1500.

To provide persistent storage for information such as data, applications, one or more operating systems, etc., a mass storage device (not shown) may also be coupled to processor 1501. In various embodiments, such mass storage devices may be implemented via Solid State Devices (SSDs) in order to achieve thinner and lighter system designs and improve system responsiveness. However, in other embodiments, the mass storage device may be implemented primarily using a Hard Disk Drive (HDD), with a smaller amount of the SSD storage device acting as an SSD cache to enable non-volatile storage of context state and other such information during a power down event, enabling fast power up upon a system activity restart. Additionally, a flash device may be coupled to processor 1501, for example, via a Serial Peripheral Interface (SPI). Such flash memory devices may provide non-volatile storage for system software, including the BIOS of the system, as well as other firmware.

Storage 1508 may include a computer-readable storage medium 1509 (also referred to as a machine-readable storage medium or computer-readable medium) on which is stored one or more sets of instructions or software (e.g., modules, units, and/or logic 1528) embodying any one or more of the methodologies or functions described herein. The processing module/unit/logic 1528 may represent any of the components described above, such as the perception module 302, the decision module 303, the planning module 304, the control module 305, and/or the machine learning engine 122 vehicle alert module 308. Processing module/unit/logic 1528 may also reside, completely or at least partially, within memory 1503 and/or within processor 1501 during execution thereof by data processing system 1500, memory 1503 and processor 1501, data processing system 1500, memory 1503 and processor 1501 also constituting machine-accessible storage media. Processing module/unit/logic 1528 may also transmit or receive over a network via network interface device 1505.

The computer-readable storage medium 1509 may also be used to permanently store some of the software functions described above. While the computer-readable storage medium 1509 is shown in an exemplary embodiment to be a single medium, the term "computer-readable storage medium" should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions. The term "computer-readable storage medium" shall also be taken to include any medium that is capable of storing or encoding a set of instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the present invention. The term "computer-readable storage medium" shall accordingly be taken to include, but not be limited to, solid-state memories, and optical and magnetic media, or any other non-transitory machine-readable medium.

The processing module/unit/logic 1528, components, and other features described herein may be implemented as discrete hardware components or integrated within the functionality of hardware components, such as ASICS, FPGAs, DSPs, or similar devices. Further, the processing module/unit/logic 1528 may be implemented as firmware or functional circuitry within a hardware device. Further, the processing module/unit/logic 1528 may be implemented in any combination of hardware devices and software components.

It should be noted that while system 1500 is illustrated with various components of a data processing system, it is not intended to represent any particular architecture or manner of interconnecting the components; as such details are not germane to embodiments of the present invention. It will also be appreciated that network computers, hand-held computers, mobile telephones, servers, and/or other data processing systems which have fewer components or perhaps more components may also be used with embodiments of the present invention.

Some portions of the foregoing detailed description have been presented in terms of algorithms and symbolic representations of operations on data bits within a computer memory. These algorithmic descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is here, and generally, considered to be a self-consistent sequence of operations leading to a desired result. The operations are those requiring physical manipulations of physical quantities.

It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise as apparent from the above discussion, it is appreciated that throughout the description, discussions utilizing terms such as those set forth in the appended claims, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system's registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices.

Embodiments of the present invention also relate to apparatuses for performing the operations herein. Such a computer program is stored in a non-transitory computer readable medium. A machine-readable medium includes any mechanism for storing information in a form readable by a machine (e.g., a computer). For example, a machine-readable (e.g., computer-readable) medium includes a machine (e.g., computer) readable storage medium (e.g., read only memory ("ROM"), random access memory ("RAM"), magnetic disk storage media, optical storage media, flash memory devices).

The processes or methods depicted in the foregoing figures may be performed by processing logic that comprises hardware (e.g., circuitry, dedicated logic, etc.), software (e.g., embodied on a non-transitory computer readable medium), or a combination of both. Although the processes or methods are described above in terms of some sequential operations, it should be appreciated that some of the operations may be performed in a different order. Further, some operations may be performed in parallel rather than sequentially.

Embodiments of the present invention are not described with reference to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of embodiments of the invention as described herein.

In the foregoing specification, embodiments of the invention have been described with reference to specific exemplary embodiments thereof. It will be evident that various modifications may be made thereto without departing from the broader spirit and scope of the invention as set forth in the following claims. The specification and drawings are, accordingly, to be regarded in an illustrative sense rather than a restrictive sense.

Claims

1. A method for operating an autonomous vehicle, the method being implemented by a computer, the method comprising:

determining that an autonomous vehicle requires deceleration based on a perception of a driving environment surrounding the autonomous vehicle;

determining a distance between the autonomous vehicle and a following vehicle that follows the autonomous vehicle and a speed of the following vehicle;

estimating a first deceleration rate required by the following vehicle to avoid a possible collision with the autonomous vehicle based on the distance and the speed of the following vehicle; and

in response to determining that the first deceleration rate is greater than a first predetermined threshold, turn on brake and emergency lights of the autonomous vehicle to alert the following vehicle that the autonomous vehicle is about to rapidly decelerate.

2. The method of claim 1, wherein the emergency lights comprise a left turn signal light and a right turn signal light.

3. The method of claim 1, further comprising: generating a braking command and issuing the braking command to the autonomous vehicle for deceleration.

4. The method of claim 3, wherein brake and emergency lights of the autonomous vehicle are turned on before issuing the braking command to the autonomous vehicle to slow down.

5. The method of claim 1, wherein the brake light is only turned on if the first deceleration rate is less than or equal to the first predetermined threshold.

6. A non-transitory machine-readable medium having instructions stored therein, which when executed by a processor, cause the processor to perform operations for maneuvering an autonomous vehicle, the operations comprising:

7. The non-transitory machine-readable medium of claim 6, wherein the emergency lights comprise a left turn signal light and a right turn signal light.

8. The non-transitory machine-readable medium of claim 6, wherein the operations further comprise: generating a braking command and issuing the braking command to the autonomous vehicle for deceleration.

9. The non-transitory machine-readable medium of claim 8, wherein the brake light and the emergency light of the autonomous vehicle are turned on before issuing the braking command to the autonomous vehicle to decelerate.

10. The non-transitory machine-readable medium of claim 6, wherein the brake light is only turned on if the first deceleration rate is less than or equal to the first predetermined threshold.

11. A data processing system comprising:

a processor; and

a memory coupled to the processor and storing instructions that, when executed by the processor, cause the processor to perform operations for maneuvering an autonomous vehicle, the operations comprising:

12. The data processing system of claim 11, wherein the emergency lights comprise a left turn signal light and a right turn signal light.

13. The data processing system of claim 11, wherein the operations further comprise: generating a braking command and issuing the braking command to the autonomous vehicle for deceleration.

14. The data processing system of claim 13, wherein a brake light and an emergency light of the autonomous vehicle are turned on before issuing the brake command to the autonomous vehicle for deceleration.

15. The data processing system of claim 11, wherein only the brake light is turned on if the first deceleration rate is less than or equal to the first predetermined threshold.